False Peaks Research Articles

Multiple-Penalty-Weighted Regularization Inversion for Dynamic Light Scattering

By using different weights to deal with the autocorrelation function data of every delay time period, the information utilization of dynamic light scattering can be obviously enhanced in the information-weighted constrained regularization inversion, but the denoising ability and the peak resolution under noise conditions for information-weighted inversion algorithm are still insufficient. On the basis of information weighting, we added a penalty term with the function of flatness constraints to the objective function of the regularization inversion, and performed the inversion of multiangle dynamic light scattering data, including the simulated data of bimodal distribution particles (466/915 nm, 316/470 nm) and trimodal distribution particles (324/601/871 nm), and the measured data of bimodal distribution particles (306/974 nm, 300/502 nm). The results of the inversion show that multiple-penalty-weighted regularization inversion can not only improve the utilization of the particle size information, but also effectively eliminate the false peaks and burrs in the inversed particle size distributions, and further improve the resolution of peaks in the noise conditions, and then improve the weighting effects of the information-weighted inversion.

Multiparametric Monitoring in Equatorian Tomato Greenhouses (III): Environmental Measurement Dynamics.

World population growth currently brings unequal access to food, whereas crop yields are not increasing at a similar rate, so that future food demand could be unmet. Many recent research works address the use of optimization techniques and technological resources on precision agriculture, especially in large demand crops, including climatic variables monitoring using wireless sensor networks (WSNs). However, few studies have focused on analyzing the dynamics of the environmental measurement properties in greenhouses. In the two companion papers, we describe the design and implementation of three WSNs with different technologies and topologies further scrutinizing their comparative performance, and a detailed analysis of their energy consumption dynamics is also presented, both considering tomato greenhouses in the Andean region of Ecuador. The three WSNs use ZigBee with star topology, ZigBee with mesh topology (referred to here as DigiMesh), and WiFi with access point topology. The present study provides a systematic and detailed analysis of the environmental measurement dynamics from multiparametric monitoring in Ecuadorian tomato greenhouses. A set of monitored variables (including CO, air temperature, and wind direction, among others) are first analyzed in terms of their intrinsic variability and their short-term (circadian) rhythmometric behavior. Then, their cross-information is scrutinized in terms of scatter representations and mutual information analysis. Based on Bland–Altman diagrams, good quality rhythmometric models were obtained at high-rate sampling signals during four days when using moderate regularization and preprocessing filtering with 100-coefficient order. Accordingly, and especially for the adjustment of fast transition variables, it is appropriate to use high sampling rates and then to filter the signal to discriminate against false peaks and noise. In addition, for variables with similar behavior, a longer period of data acquisition is required for the adequate processing, which makes more precise the long-term modeling of the environmental signals.

Highly Accurate Detection and Identification Methodology of Xenobiotic Metabolites Using Stable Isotope Labeling, Data Mining Techniques, and Time-Dependent Profiling Based on LC/HRMS/MS.

A generally applicable method to discover xenobiotic metabolites is important to safely and effectively develop xenobiotics. We propose an advanced method to detect and identify comprehensive xenobiotic metabolites using stable isotope labeling, liquid chromatography coupled with benchtop quadrupole Orbitrap high-resolution tandem mass spectrometry (LC/HRMS/MS), data mining techniques (alignment, peak picking, and paired-peaks filtering), in silico metabolism prediction, and time-dependent profiling. The LC/HRMS analysis was carried out using Arabidopsis T87 cultured cells treated with unlabeled or with 13C- or 2H-labeled 2,4-dichlorophenoxyacetic acid (2,4-D). Paired-peak filtering enabled the accurate detection of 83 candidates for 2,4-D metabolites without any false positive peaks derived from solvents or the biological matrix. We confirmed 10 previously reported 2,4-D metabolites and identified 16 novel 2,4-D metabolites. Our method provides accurate detection and identification of comprehensive xenobiotic metabolites and represents a potentially useful tool for elucidating xenobiotic metabolism.

Digital beamforming for ultra‐wideband signals utilizing an extrapolated array generated by Carathéodory representation combining fractional delay filters based on high‐order Hermite interpolation

Digital beamforming (DBF) for ultra‐wideband (UWB) signals plays a key role in many applications, and high spatial resolution and accurate digital delay of UWB signals are the focus of UWB DBF research. In this paper, we propose a novel DBF method utilizing an extrapolated array generated by Carathéodory representation combining fractional delay filters (FDFs) based on high‐order Hermite interpolation. The Carathéodory representation could extrapolate the aperture of the original uniform linear array (ULA) significantly and provide a better real‐time performance than the existing extrapolation technique based on the two‐dimensional autoregressive (2‐D AR) model. The FDFs based on high‐order Hermite interpolation not only improve the DBF performance when the frequency of the signals is close to the Nyquist rate but also reduce the sampling rate to half the Nyquist rate. The UWB linear frequency‐modulated (LFM) signal is used for simulation analysis. In contrast with traditional DBF methods and FDFs based on Lagrange interpolation, the proposed method has a much higher spatial resolution, has no false peak, and requires fewer sensors, as demonstrated by simulation results. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Expelled oils and their impacts on Rock-Eval data interpretation, Eocene Qianjiang Formation in Jianghan Basin, China

Source rock samples can be “contaminated” by expelled oils from nearby mature source rocks or from the organic-rich laminae within the same source rock units. The “contaminations” on the Rock-Eval samples could produce a false high S1 peak, a high Production Index (PI), a suppressed Tmax and other anomalies. Identifying the samples affected by expelled oils, assessing and correcting the impacts are crucial in source rock study for shale oil/shale gas resource evaluation. This study analyzes the Rock-Eval 6 results of 43 core samples from two recent shale oil exploration wells of the Eocene Qianjiang Formation, a confined source rock unit embedded with salt intervals formed in a hypersaline lacustrine setting in central China. The geochemical anomalies of the affected samples are shown through the available samples in this study. Criteria for identifying the affected samples are discussed, and impacts on the conventional Rock-Eval parameters and subsequent estimation of kinetic parameters presented. This study provides insights into Rock-Eval data interpretation, particularly in source rock evaluation for estimating shale oil resources.

A new method for removing false peaks to obtain a precise X-ray spectrum

To get a precise X-ray energy spectrum from the latest high-performance silicon drift detector (fast SDD), a switch reset preamplifier circuit, which has a high signal to noise ratio and small ballistic loss, is used to amplify the weak signal transmitted by the detector. Aiming at the technical problem of fast SDD, which works at high-count rate conditions, we adopt a slow triangle shaping method and use switch reset type preamplifier, and a new method is put forward to remove the false peaks to obtain a precise X-ray spectrum, in essence, to eliminate the distorted pulses transmitted by the detector. 55Fe standard source and a certain kind of rock sample are regarded as measuring objects in the experiments. The spectral comparison figure, which contains the two measurement results of the pre and post elimination of the false peaks, respectively, shows that this method removes the false peaks located in the front of the full-energy peak in spectra and improves the peak-to-background ratio in a complex spectral analysis and the analytical precision of weak signals.

A novel segmentation method for uneven lighting image with noise injection based on non-local spatial information and intuitionistic fuzzy entropy

Local thresholding methods for uneven lighting image segmentation always have the limitations that they are very sensitive to noise injection and that the performance relies largely upon the choice of the initial window size. This paper proposes a novel algorithm for segmenting uneven lighting images with strong noise injection based on non-local spatial information and intuitionistic fuzzy theory. We regard an image as a gray wave in three-dimensional space, which is composed of many peaks and troughs, and these peaks and troughs can divide the image into many local sub-regions in different directions. Our algorithm computes the relative characteristic of each pixel located in the corresponding sub-region based on fuzzy membership function and uses it to replace its absolute characteristic (its gray level) to reduce the influence of uneven light on image segmentation. At the same time, the non-local adaptive spatial constraints of pixels are introduced to avoid noise interference with the search of local sub-regions and the computation of local characteristics. Moreover, edge information is also taken into account to avoid false peak and trough labeling. Finally, a global method based on intuitionistic fuzzy entropy is employed on the wave transformation image to obtain the segmented result. Experiments on several test images show that the proposed method has excellent capability of decreasing the influence of uneven illumination on images and noise injection and behaves more robustly than several classical global and local thresholding methods.

Histological validation of diffusion MRI fiber orientation distributions and dispersion

Diffusion magnetic resonance imaging (dMRI) is widely used to probe tissue microstructure, and is currently the only non-invasive way to measure the brain's fiber architecture. While a large number of approaches to recover the intra-voxel fiber structure have been utilized in the scientific community, a direct, 3D, quantitative validation of these methods against relevant histological fiber geometries is lacking. In this study, we investigate how well different high angular resolution diffusion imaging (HARDI) models and reconstruction methods predict the ground-truth histologically defined fiber orientation distribution (FOD), as well as investigate their behavior over a range of physical and experimental conditions. The dMRI methods tested include constrained spherical deconvolution (CSD), Q-ball imaging (QBI), diffusion orientation transform (DOT), persistent angular structure (PAS), and neurite orientation dispersion and density imaging (NODDI) methods. Evaluation criteria focus on overall agreement in FOD shape, correct assessment of the number of fiber populations, and angular accuracy in orientation. In addition, we make comparisons of the histological orientation dispersion with the fiber spread determined from the dMRI methods. As a general result, no HARDI method outperformed others in all quality criteria, with many showing tradeoffs in reconstruction accuracy. All reconstruction techniques describe the overall continuous angular structure of the histological FOD quite well, with good to moderate correlation (median angular correlation coefficient > 0.70) in both single- and multiple-fiber voxels. However, no method is consistently successful at extracting discrete measures of the number and orientations of FOD peaks. The major inaccuracies of all techniques tend to be in extracting local maxima of the FOD, resulting in either false positive or false negative peaks. Median angular errors are ∼10° for the primary fiber direction and ∼20° for the secondary fiber, if present. For most methods, these results did not vary strongly over a wide range of acquisition parameters (number of diffusion weighting directions and b value). Regardless of acquisition parameters, all methods show improved successes at resolving multiple fiber compartments in a voxel when fiber populations cross at near-orthogonal angles, with no method adequately capturing low to moderate angle (<60°) crossing fibers. Finally, most methods are limited in their ability to capture orientation dispersion, resulting in low to moderate, yet statistically significant, correlation with histologically-derived dispersion with both HARDI and NODDI methodologies. Together, these results provide quantitative measures of the reliability and limitations of dMRI reconstruction methods and can be used to identify relative advantages of competing approaches as well as potential strategies for improving accuracy.

Sensors and Signal Processing Methods for a Wearable Physiological Parameters Monitoring System

The present paper presents some of the specific aspects of the development of a smartphone-based physiological parameters monitoring system intended for occupational safety applications. The system consists of a comfortable garment with integrated wireless sensors and a personal smartphone connected to the sensors via Bluetooth. A smartphone is used for data gathering and processing, heart and respiratory rate evaluation and information transmission. A reliability of the physiological parameter values obtained by the system highly depends on the signal processing and evaluation methods applied. The methods must ensure robustness for human motion artefacts in the signal and other noise prevailing in industrial environment. The monitoring system presented here combines relatively simple data gathering tools with certain novel digital signal processing algorithms that are useful in solving reliability problems caused by human motion artefacts and electromagnetic interference. The proposed method for the evaluation of respiratory and heart rate and the developed algorithms are based on the detection of distinctive peaks in respiratory and electrocardiogram signals using digital filtering and a moving difference window with an adaptive length. A median threshold criterion is applied to reject the false peaks in those signals caused by noise. The sensors and data processing algorithms were tested experimentally and the results were verified with a portable medical patient monitor MEC-1000. The differences between the values of corresponding parameters were insignificant, which means that the indications of the wearable monitoring system could be trustworthy and practically applicable.DOI: http://dx.doi.org/10.5755/j01.eie.23.5.19245

A new surface fractal dimension for displacement mode shape-based damage identification of plate-type structures

Abstract Vibration-based nondestructive testing is an area of growing interest and worthy of exploring new and innovative approaches. The displacement mode shape is often chosen to identify damage due to its local detailed characteristic and less sensitivity to surrounding noise. Requirement for baseline mode shape in most vibration-based damage identification limits application of such a strategy. In this study, a new surface fractal dimension called edge perimeter dimension (EPD) is formulated, from which an EPD-based window dimension locus (EPD-WDL) algorithm for irregularity or damage identification of plate-type structures is established. An analytical notch-type damage model of simply-supported plates is proposed to evaluate notch effect on plate vibration performance; while a sub-domain of notch cases with less effect is selected to investigate robustness of the proposed damage identification algorithm. Then, fundamental aspects of EPD-WDL algorithm in term of notch localization, notch quantification, and noise immunity are assessed. A mathematical solution called isomorphism is implemented to remove false peaks caused by inflexions of mode shapes when applying the EPD-WDL algorithm to higher mode shapes. The effectiveness and practicability of the EPD-WDL algorithm are demonstrated by an experimental procedure on damage identification of an artificially-induced notched aluminum cantilever plate using a measurement system of piezoelectric lead-zirconate (PZT) actuator and scanning laser Doppler vibrometer (SLDV). As demonstrated in both the analytical and experimental evaluations, the new surface fractal dimension technique developed is capable of effectively identifying damage in plate-type structures.

Performance Analysis of Accurate Time Delay and Its Influence on Sonar Beam Formation

The time delay accuracy affects the processing performance of the sonar signals. This paper studies the implementation principles of such accurate time delay methods as linear interpolation method, Sinc function interpolation method, Lagrange interpolation method and Farrow filter method and investigates the performance of different accurate time delay methods by simulation as well as the influence of accurate time delay on beam formation. The research results show the images of accurate time delay will be smoother, and lower in side lobe and less in false peak than that of conventional time delay. But both of them have the same beam width.

Coherent side‐band BOC processing

Binary offset carrier (BOC) modulated signals are characterised by multi-peaked correlation functions which may lead to false peak locks that occur when a secondary correlation lobe is tracked instead of the main peak. To solve this problem, side-band processing (SBP) operates on the two main lobes of the BOC spectrum separately, in a binary phase shift keying like fashion. Although an unambiguous correlation function is obtained, the information provided by the BOC subcarrier is lost. This information is preserved by the double estimator and by the double phase estimator (DPE) which adopt an additional tracking loop to process the subcarrier component. In this study, the connection between SBP and independent subcarrier processing is shown and a novel SBP scheme, the coherent side-band (CSB) approach, is proposed. As in standard SBP, the two main lobes of the BOC spectrum are processed separately and upper and lower correlators are computed. These correlators are then combined to recover the subcarrier information. CSB provides subcarrier measurements and achieves the performance of the DPE. CSB tracking is thoroughly characterised and simulations and real-data processing are used to support theoretical findings. The CSB approach is an effective alternative to independent subcarrier processing.

Detailed Investigation and Comparison of the XCMS and MZmine 2 Chromatogram Construction and Chromatographic Peak Detection Methods for Preprocessing Mass Spectrometry Metabolomics Data.

XCMS and MZmine 2 are two widely used software packages for preprocessing untargeted LC/MS metabolomics data. Both construct extracted ion chromatograms (EICs) and detect peaks from the EICs, the first two steps in the data preprocessing workflow. While both packages have performed admirably in peak picking, they also detect a problematic number of false positive EIC peaks and can also fail to detect real EIC peaks. The former and latter translate downstream into spurious and missing compounds and present significant limitations with most existing software packages that preprocess untargeted mass spectrometry metabolomics data. We seek to understand the specific reasons why XCMS and MZmine 2 find the false positive EIC peaks that they do and in what ways they fail to detect real compounds. We investigate differences of EIC construction methods in XCMS and MZmine 2 and find several problems in the XCMS centWave peak detection algorithm which we show are partly responsible for the false positive and false negative compound identifications. In addition, we find a problem with MZmine 2's use of centWave. We hope that a detailed understanding of the XCMS and MZmine 2 algorithms will allow users to work with them more effectively and will also help with future algorithmic development.

One Step Forward for Reducing False Positive and False Negative Compound Identifications from Mass Spectrometry Metabolomics Data: New Algorithms for Constructing Extracted Ion Chromatograms and Detecting Chromatographic Peaks.

False positive and false negative peaks detected from extracted ion chromatograms (EIC) are an urgent problem with existing software packages that preprocess untargeted liquid or gas chromatography-mass spectrometry metabolomics data because they can translate downstream into spurious or missing compound identifications. We have developed new algorithms that carry out the sequential construction of EICs and detection of EIC peaks. We compare the new algorithms to two popular software packages XCMS and MZmine 2 and present evidence that these new algorithms detect significantly fewer false positives. Regarding the detection of compounds known to be present in the data, the new algorithms perform at least as well as XCMS and MZmine 2. Furthermore, we present evidence that mass tolerance in m/z should be favored rather than mass tolerance in ppm in the process of constructing EICs. The mass tolerance parameter plays a critical role in the EIC construction process and can have immense impact on the detection of EIC peaks.

Untargeted LC–MS Data Preprocessing in Metabolomics

Liquid chromatography–mass spectrometry (LC–MS) has enabled the detection of thousands of metabolite features from a single biological sample that produces large and complex datasets. One of the key issues in LC–MS-based metabolomics is comprehensive and accurate analysis of enormous amount of data. Many free data preprocessing tools, such as XCMS, MZmine, MAVEN, and MetaboAnalyst, as well as commercial software, have been developed to facilitate data processing. However, researchers are challenged by the inevitable and unconquerable yields of numerous false-positive peaks, and human errors while manually removing such false peaks. Even with continuous improvements of data processing tools, there can still be many mistakes generated during data preprocessing. In addition, many data preprocessing software exist, and every tool has its own advantages and disadvantages. Thereby, a researcher needs to judge what kind of software or tools to choose that most suit their vendor proprietary formats and goal of downstream analysis. Here, we provided a brief introduction of the general steps of raw MS data processing, and properties of automated data processing tools. Then, characteristics of mainly free data preprocessing software were summarized for researchers’ consideration in conducting metabolomics study.

Measuring of False Peaks Occurring via Planar Antenna Arrays DOA Estimation

Direction-of-arrival (DOA) of radio signals takes a great scientific interest for radar, sonar and wireless communication tasks. In this paper the problem of DOA estimation methods for circular and concentric circular antenna arrays is considered. The super-resolution method MUSIC is researched. The performances are estimated in various noise environments and for various geometries of antenna arrays. Additionally, the problem of false peaks occurring in spatial spectrum is closely considered. Probability of occurring false peaks after computer simulations is presented.

Empirical consideration of the effects of acquisition parameters and analysis model on clinically feasible q-ball imaging

Q-ball imaging (QBI) is a popular high angular resolution diffusion imaging (HARDI) technique used to study brain architecture in vivo. Simulation and phantom-based studies suggest that QBI results are affected by the b-value, the number of diffusion weighting directions, and the signal-to-noise ratio (SNR). However, optimal acquisition schemes for QBI in clinical settings are largely undetermined given empirical (observed) imaging considerations. In this study, we acquire a HARDI dataset at five b-values with 11 repetitions on a single subject to investigate the effects of acquisition scheme and subsequent analysis models on the accuracy and precision of measures of tissue composition and fiber orientation derived from clinically feasible QBI at 3T. Clinical feasibility entails short scan protocols - less than 5minutes in the current study - resulting in lower SNR, lower b-values, and fewer diffusion directions than are typical in most QBI protocols with research applications, where time constraints are less prevalent. In agreement with previous studies, we find that the b-value and number of diffusion directions impact the magnitude and variation of QBI indices in both white matter and gray matter regions; however, QBI indices are most heavily dependent on the maximum order of the spherical harmonic (SH) series used to represent the diffusion orientation distribution function (ODF). Specifically, to ensure numerical stability and reduce the occurrence of false peaks and inflated anisotropy, we recommend oversampling by at least 8–12 more diffusion directions than the number of estimated coefficients for a given SH order. In addition, in an equal scan time comparison of QBI accuracy, we find that increasing the directional resolution of the HARDI dataset is preferable to repeating observations; however, our results indicate that as few as 32 directions at a low b-value (1000s/mm2) captures most of the angular information in the q-ball ODF. Our findings provide guidance for determining an optimal acquisition scheme for QBI in the low SNR and low scan time regime, and suggest that care must be taken when choosing the basis functions used to represent the QBI ODF.

DOA Estimation of Coherent Signals on Coprime Arrays Exploiting Fourth-Order Cumulants.

This paper considers the problem of direction-of-arrival (DOA) estimation of coherent signals on passive coprime arrays, where we resort to the fourth-order cumulants of the received signal to explore more information. A fourth-order cumulant matrix (FCM) is introduced for the coprime array. The special structure of the FCM is combined with the array configuration to resolve the coherent signals. Since each sparse array of a coprime array is uniform, a series of overlapping identical subarrays can be extracted. Using this property, we propose a generalized spatial smoothing scheme applied to the FCM. From the smoothed FCM, the DOAs of both the coherent and independent signals can be successfully estimated on the pseudo-spectrum generated by the fourth-order MUSIC algorithm. To overcome the problem of occasional false peaks appearing on the pseudo-spectrum, we use a supplementary sparse array whose inter-sensor spacing is coprime to that of either existing sparse array. From the combined spectrum aided by the supplementary sensors, the false peaks are removed while the true peaks remain. The effectiveness of the proposed methods is demonstrated by simulation examples.

Mass Spectral Feature List Optimizer (MS-FLO): A Tool To Minimize False Positive Peak Reports in Untargeted Liquid Chromatography-Mass Spectroscopy (LC-MS) Data Processing.

Untargeted metabolomics by liquid chromatography-mass spectrometry generates data-rich chromatograms in the form of m/z-retention time features. Managing such datasets is a bottleneck. Many popular data processing tools, including XCMS-online and MZmine2, yield numerous false-positive peak detections. Flagging and removing such false peaks manually is a time-consuming task and prone to human error. We present a web application, Mass Spectral Feature List Optimizer (MS-FLO), to improve the quality of feature lists after initial processing to expedite the process of data curation. The tool utilizes retention time alignments, accurate mass tolerances, Pearson's correlation analysis, and peak height similarity to identify ion adducts, duplicate peak reports, and isotopic features of the main monoisotopic metabolites. Removing such erroneous peaks reduces the overall number of metabolites in data reports and improves the quality of subsequent statistical investigations. To demonstrate the effectiveness of MS-FLO, we processed 28 biological studies and uploaded raw and results data to the Metabolomics Workbench website ( www.metabolomicsworkbench.org ), encompassing 1481 chromatograms produced by two different data processing programs used in-house (MZmine2 and later MS-DIAL). Post-processing of datasets with MS-FLO yielded a 7.8% automated reduction of total peak features and flagged an additional 7.9% of features, per dataset, for review by the user. When manually curated, 87% of these additional flagged features were verified false positives. MS-FLO is an open source web application that is freely available for use at http://msflo.fiehnlab.ucdavis.edu .

Effects of tissue mechanical and acoustic anisotropies on the performance of a cross-correlation-based ultrasound strain imaging method

The anisotropic mechanical properties (mechanical anisotropy) and view-dependent ultrasonic backscattering (acoustic anisotropy) of striated muscle due to the underlying myofiber arrangement have been well documented, but whether they impact on ultrasound strain imaging (USI) techniques remains unclear. The aim of this study was therefore to investigate the performance of a cross-correlation-based two-dimensional (2D) USI method in anisotropic media under controlled quasi-static compression in silico and in vitro. First, synthetic pre- and post-deformed 2D radiofrequency images of anisotropic phantoms were simulated in two scenarios to examine the individual effect of the mechanical and acoustic anisotropies on strain estimation. In the first scenario, the phantom was defined to be transversely isotropic with the scatterer amplitudes following a zero-mean Gaussian distribution, while in the second scenario, the phantom was defined to be mechanically isotropic with Gaussian distributed scatterer amplitudes correlated along the principal directions of pre-defined fibers. These two anisotropies were then jointly incorporated into the ultrasound image simulation model with additional depth-dependent attenuation. Three imaging planes—the fiber plane with the fiber direction perpendicular to the ultrasound beam (TISperp_fb), the fiber plane with the fiber direction parallel to the beam (TISpara), and the transverse fiber plane (TISperp_cfb)—were studied. The absolute relative error (ARE) of the lateral strain estimates in TISperp_fb (20.99 ± 15.65%) was much higher than that in TISperp_cfb (4.14 ± 3.17%). The ARE in TISpara was unavailable owing to the large spatial extent of false peaks. The effect of tissue anisotropy on the performance of the 2D USI was further confirmed in an in vitro porcine skeletal muscle phantom. The best in-plane strain quality was again shown in TISperp_cfb (elastographic signal-to-noise ratio, or SNRe: >25 dB), whereas the most unreliable strain estimates were found as expected in TISpara (SNRe: <10 dB). The strain filter explained the effect of the mechanical anisotropy and required the underlying strain to be within an optimal range for estimation. Sonographic SNR (SNRs) was found to be altered by the acoustic anisotropy and was much lower in TISpara (~10 dB) than in TISperp_fb (~50 dB) in vitro, which affected the accuracy of the strain estimation. Speckle size showed no evident impact on strain estimation but requires further examination.